Optimal Power Distribution Research Articles

Advanced energy management strategy for microgrids with integrated battery storage and renewable generation

As electric mobility gains popularity, Electric Vehicles (EVs) and their batteries are becoming more attractive due to their size and energy density advantages. However, the electric grid has not undergone similar improvements, potentially impacting power stability and affecting EV energy usage and availability. The key challenge lies in managing increasing power demands from a fully EV fleet. To address this, efforts are needed to analyze the integration of EVs into the grid and optimize power distribution. In this paper, an innovative Energy Management Strategy (EMS) is proposed to effectively control energy loads, energy sources, and EVs, incorporating Vehicle-to-Grid (V2G) capability. The EMS optimizes energy flow and storage based on time of day, potential energy production, and the cost of grid electricity. The integration of this EMS results in significant benefits, with approximately 12% savings in electricity bills compared to a reference strategy.

Optimization of aircraft electrothermal de-icing power distribution based on genetic algorithm

Optimization of aircraft electrothermal de-icing power distribution based on genetic algorithm

Optimized Power Distribution using Machine Learning for Load Forecasting, Fault Detection, and Voltage Regulation

The optimization of electric power distribution systems is crucial for enhancing efficiency, reliability, and sustainability in modern power networks. Traditional optimization methods often struggle to handle the complexity and variability of large-scale power distribution grids. With the advent of ML, new opportunities have emerged to address these challenges more effectively. This paper explores the application of machine learning algorithms in optimizing power distribution systems, focusing on load forecasting, fault detection, voltage regulation, and network reconfiguration. By employing supervised, unsupervised, and reinforcement learning techniques, ML models can process vast amounts of real-time data, identify patterns, and make accurate predictions for system performance enhancement. This study presents a comprehensive review of recent advancements in ML-based optimization techniques, emphasizing their ability to improve the accuracy of load demand forecasts and reduce energy losses. Moreover, it discusses the integration of smart grid technology with ML models to enable adaptive control strategies that can respond to dynamic power demands. Various case studies and simulation results are included to demonstrate the practical benefits of machine learning applications in electric power distribution. The findings suggest that incorporating machine learning into the power distribution framework can significantly boost operational efficiency, reduce downtime, and facilitate the transition to a more intelligent and sustainable power grid. This paper concludes with a discussion of the challenges and future prospects of ML in electrical grid optimization, such as scalability, data privacy, and the need for real-time computation.

Cooperative maximum adhesion tracking control for multi-motor electric locomotives

PurposeThis study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives. The method is intended to optimize the output torque of each motor, maximize the utilization of train adhesion within the total torque command, reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle, thus realizing the optimal allocation of the multi-motor electric locomotives.Design/methodology/approachIn this study, a model predictive control (MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented. Firstly, train traction system with multiple motors is constructed in accordance with Newton’s second law. These equations include the train dynamics equations, the axle dynamics equations, and the wheel-rail adhesion coefficient equations. Then, a new MPC-based multi-axle adhesion co-optimization method is put forward. This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.FindingsThis paper presents a MPC system designed for the cooperative control of multi-axle adhesion. The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.Originality/valueThis study presents a novel cooperative adhesion tracking control scheme. It is designed for multi-motor electric locomotives, which has rarely been studied before. And simulations are carried out in different conditions, including variable surfaces and motor failing.

The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

Probabilistic Time Series Forecasting Based on Similar Segment Importance in the Process Industry

Probabilistic time series forecasting is crucial in various fields, including reducing stockout risks in retail, balancing road network loads, and optimizing power distribution systems. Building forecasting models for large-scale time series is challenging due to distribution differences, amplitude fluctuations, and complex patterns across various series. To address these challenges, a probabilistic forecasting method with two different implementations that focus on historical segment importance is proposed in this paper. First, a patch squeeze and excitation (PSE) module is designed to preprocess historical data, capture segment importance, and distill information. Next, an LSTM-based network is used to generate maximum likelihood estimations of distribution parameters or different quantiles for multi-step forecasting. Experimental results demonstrate that the proposed PSE module significantly enhances the base model’s prediction performance, and direct multi-step forecasting offers more detailed information for high-frequency data than recursive forecasting.

An Enhancement of Grid Integration in Renewable Energy Systems Using Multi- Objective Multi-Verse Optimization

Going by the recent trends, the application of Renewable Energy Sources (RESs) has grown significantly across the world. Still, the integration of grid with photovoltaic (PV), wind and battery, remains a critical challenge as it results into power quality issues. To address the increasing need for electricity caused by industrialization and growing population, hybrid PV, wind, and battery combinations are used in this study. To accomplish an optimal energy management, this research proposes the multi- objective multi-verse optimization (MOMVO) approach, along with the modified perturb and observe (MP&O) technique. The proposed MOMVO-MP&O controller operates between the wind turbine and the battery storage system, for providing optimal power distribution and stability. The suggested model is evaluated alongside three other popular controller combinations that are, multi-verse optimization-perturb and observe (MVO-P&O), MVO-MP&O, and MOMVO-P&O. A comparative analysis is conducted, with existing methods namely, Modified-Fuzzy Direct Power Control (MF-DPC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) also. From the analyzed results of this comparison, the proposed MOMVO-MP&O achieves lesser Total Harmonic Distortion (THD) of 1.86%, which demonstrates its efficiency in addressing power quality issues using hybrid RES systems.

Techno-economic performance of different DC power distribution networks in official buildings based on a real-time analysis method

The integration of photovoltaic (PV) and energy storage systems into official buildings has garnered considerable attention, which are recognized as DC power sources. Notably, DC power can satisfy the majority of electrical demands in official buildings. The primary objective of this research is to enhance overall system efficiency by efficiently utilizing renewable energy through direct current (DC) power distribution compared to alternating current (AC) systems. This study proposes a techno-economic analysis framework to guide designers in selecting system configuration and voltage levels. The analysis covers various PV capacities, load compositions, and battery capacities, with the aim of optimizing power distribution efficiency and achieving a more competitive levelized cost of energy (LCOE). The proposed model integrates the dynamic efficiency of actual converters and wire gauges, enabling real-time computation of the energy supply–demand balance within the power distribution system, including associated conversion and transmission losses. The results indicate that conversion loss accounts for approximately 80% of the overall energy loss within power distribution networks. The implementation of DC power distribution significantly improves system efficiency and economic viability. Specifically, the DC375 system exhibits superior techno-economic performance in scenarios involving high PV and battery capacities. In scenarios with limited PV and battery capacity, the building power distribution system should employ a hybrid AC-DC topology to efficiently distribute the available PV output to building loads powered by DC.

Dynamic energy management and control of networked microgrids based on load to grid services and incentive-based demand response programs: A multi-agent deep reinforcement learning approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Influence of intensity distributions on the process dynamics during laser deep penetration welding of pure nickel with a flexible ring mode laser source

Current research results demonstrate the potential of spatter reduction in laser deep penetration welding by using adapted laser beam intensity distributions between a ring and a core intensity. The spatter-reducing effect, resulting from the additional ring intensity, is often attributed to an enlarged keyhole opening. However, it remains a matter of debate how exactly an additional ring intensity influences the process. This study, therefore, investigates the effect of an increasing ring intensity on process dynamics and the spatter formation regime. Laser beam welding experiments were carried out on 2.4068 nickel hidden T-joints at different welding speeds of up to 16 m/min, as well as different intensity distributions, while maintaining a constant weld depth. The tests were evaluated by using two high-speed cameras from different angles to analyze spatter and their formation mechanisms as well as the keyhole opening. Metallographic analyses and x-ray images were used to determine influences on the seam shape and porosity. It was shown that a change in intensity distribution has a significant influence on the melt flow, the resulting amount of spatter, and the porosity, which is comparable across the investigated welding speeds. While a core-based power distribution leads to spatter formation on the rear of the keyhole opening, it was shown that a ring-based power distribution leads to spatter formation on both sides of the keyhole. With an optimal power distribution, spatter formation could be largely prevented. Based on these findings, the single wave regime, a stabilized regime, and a lateral spatter regime were identified.

Abusive litigation in China: comparative insights and practical approaches for reform

This article provides a comprehensive examination of abusive litigation in both civil and criminal cases in China, focusing on its forms and impacts. It proposes nuanced regulatory approaches that emphasize the importance of identifying responsible parties and implementing appropriate compensation measures. In the Chinese legal system, abusive litigation varies by context. In civil cases, it often manifests as actions that clog the courts and drain legal resources, raising concerns about lawyer accountability and the financial burdens on victims. Although frequently discussed in Chinese literature, the above topic has seen limited comparative analysis, despite similar problems existing across different jurisdictions and a clear need for mutual learning. The article suggests that, in civil cases, insights from English and Dutch law could assist Chinese regulators in reducing incentives for lawyers to pursue unmeritorious or abusive cases and improving compensation for victims. In the criminal law context, abusive litigation is more prevalent in minor offenses in China, primarily due to issues related to the public power structure. The proposal emphasizes the need to optimize power distribution within the Chinese legal system, thereby safeguarding the integrity of the law and upholding human rights.

Efficiency evaluation of electricity demand prediction models in lower-income countries: a case study of panama

Abstract Electricity plays a crucial role in the development of countries, serving as the foundation for the functioning of modern societies and as a key indicator of a nation’s level of modernization and overall strength. Predicting a country’s electricity demand provides valuable insights into the evolving patterns of electrical energy provision and usage and helps to improve the flexibility of the energy distribution network. In addition, it provides early warnings and decision support for governments, businesses, and individuals about future electricity consumption. This plays a pivotal function in optimizing power distribution, adjusting the proportion of power generation sources, and ensuring a continuous and secure electricity supply. This paper uses the example of electricity demand in Panama and uses the LSTM model and Prophet model to compare their predictions for electricity demand in lower-income countries, with the aim of improving accuracy. Furthermore, it introduces a data set representing the electricity demand of higher-income countries, using the United Kingdom as a representative, to compare the forecast results between the two countries. The results indicate that within the Panama data set, the Prophet model exhibits better accuracy than the LSTM model. On the contrary, in the data set representing higher-income countries with the United Kingdom as a representative, the LSTM model outperforms the Prophet model. This leads to the conclusion that the selection of a predictive model is specific to each country, influenced by factors such as development status, geographic location, policies, energy markets, and more. A specific model may not be universally applicable due to these unique conditions, requiring tailored-modeling approaches for precise and efficient allocation and scheduling of electricity resources. This approach ensures the improvement of residents’ living standards and quality of life while meeting the typical demands of a country’s economic and social development.

OPTIMAL DISTRIBUTION OF ADDITIONAL ELECTRICAL POWER

The problem of optimal distribution of additional electric power for private houses and households with solar panels or stations in adverse weather conditions over a long period of time is considered. This problem is very relevant for regions where the supply of electric power to houses and households is carried out mainly by green technology. Based on this, it is shown that when weather conditions worsen, which can last for a long time, a problem arises of providing these objects with energy allocated from backup power lines, the capacity of which is insufficient for full energy supply. To solve this problem, especially in the case of a shortage of allocated power, when it is not enough to satisfy all consumers to the maximum, the criteria for distributing the allocated limited power among houses are analyzed, examples are given for calculating the residual energy of a deep-discharge battery for each household in the event of prolonged bad weather conditions. Two options for solving the problem of optimal distribution of the allocated power are considered, taking into account the full simulation model of houses and households with electricity supply from solar panels: - distribution by residual energy taking into account the energy required to charge the batteries of private houses; - distribution by residual energy without charging the batteries. The solution to the problem of distributing limited energy between houses with solar panels is shown using the example of 5 households. For this purpose, the initial conditions and restrictions for each house are modeled. The problem of optimal distribution of allocated energy between these objects is formulated. The model of the problem itself is built and solved using the SOLVER subroutine of the Excel program. The main tables are given, in which it is necessary to enter restrictions, optimization criteria and the objective function, screenshots of the solution of this problem in two versions with the introduction of all restrictions in separate Excel windows. It is shown that solution of the issue of optimal distribution of additional energy allocated to eliminate the shortage of energy in conditions of limitation allows to the following opportunities: - provision of additional energy depending on the time of complete depletion of energy in the house prevents overloading of the electric network, incorrect openings in power lines and other unpleasant situations; - the inclusion of the module of optimal distribution of additional energy into the model during simulation, allows to save time for the seeking of additional energy and thus to increase the overall efficiency of the system. Keywords: solar panels, bad weather, additional energy, optimal distribution.

An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

CO2 emissions of fuel-cell battery hybrid system for large ships

ABSTRACT The needs of solid oxide fuel cells (SOFC) and batteries in large ships are analyzed by estimating the amount of fuel consumption and CO2 emissions. Three types of systems are proposed: 1) fuel cells-battery-generator engine system, 2) battery-generator engine system, and 3) generator engine system. This study is based on the time-varying electric power demand and adopted the sequential quadratic programming (SQP) method to find the optimal power distribution of mixed power sources. CO2 emissions in the SOFC hybrid system are 11.6% lower than in the conventional generator engine system, and those in the battery-generator engine system are 5.1% lower than in the conventional system. The reduction of CO2 emissions come from three factors: the operation of the generator engine at a relatively high load rate, the battery sharing the operation of the generator engine, and the high efficiency of the SOFC running. The SOFC hybrid system can be a sustainable technology in large vessels.

Optimal operation of soft open point devices and distribution network reconfiguration in a harmonically polluted distribution network

Soft open point (SOP) and distribution network reconfiguration (DNR) are crucial for modern power systems as they address significant challenges in managing power losses and maintaining power quality. SOP is a power electronic device used in distribution networks (DNs) instead of tie lines. It injects reactive power into feeders and transfers active power between them, while DNR involves altering the network topology to improve DN index. In this paper, the effect of DNR, optimal operation of SOP, and simultaneous operation of DNR and SOP are thoroughly investigated and compared on IEEE 33 bus harmonic polluted DN via particle swarm optimization (PSO). These comparisons were performed by considering three loss-based objectives including fundamental and harmonic losses and summation of them, along with one voltage-based objective function assessing voltage stability, and four Total Harmonic Distortion (THD)-based objective functions quantifying harmonic pollution. Simulation results not only demonstrate the importance of optimal operation of SOP in improving losses and power quality but also highlight the synergistic effect of simultaneous operation of DNR and SOP in further enhancing these two objectives. Results provide valuable insights for future research and practical applications in operation and optimizing power distribution systems.

A Redundant Secondary Control Scheme to Resist FDI Attacks in AC Microgrids

Hierarchical control of AC microgrids, despite providing stable voltage, frequency, and optimal power distribution, is susceptible to false data injection (FDI) attacks on its secondary control. Electric power systems in countries such as the United States and Venezuela have suffered cyberattacks. This paper proposes a redundant secondary control method to resist FDI attacks. This method uses distributed generators (DGs) online as redundant units on standby, generating dynamic corrections to the attacked DGs, thus eliminating the attack impact. It effectively addresses the numerical tampering and addition brought by FDI to the frequency and voltage setting values of the secondary control, ensuring the microgrid can still output stable frequency and voltage. The principle of this control method is direct and practical; it doesn't require complex controllers and can respond quickly to FDI attacks. Then, focusing on a microgrid system containing six DGs, the setting values of the microgrid system are tampered by multiple values in different time periods, the simulation results confirm that the proposed method can effectively resist FDI attacks under various scenarios. Finally, FPGA-in-the-loop experiments further verify the effectiveness of the proposed method.

Optimal distribution of reactive power by hybrid metaheuristic methods applied to the west Algerian network

Optimal distribution of reactive power by hybrid metaheuristic methods applied to the west Algerian network

Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle

In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.

Reinforcement learning control of multi-spectral LED systems for rendering optimal lighting performance in indoor environment

Light-emitting diodes (LEDs) have superseded traditional light sources due to their superior qualities. However, achieving desired spectral distribution in indoor environment still remains a challenge, particularly for optimizing multiple lighting parameters, which are nonlinear functions of the light spectra, including correlated color temperature (CCT), color rendering index (CRI) and circadian action factor (CAF). Existing studies have been mainly devoted to seeking optimal solutions, by means of various optimization methods, e.g. genetic algorithms. However, these methods can only result in fixed recipes to achieve pre-assigned reference parameters, and cannot meet the need of dynamic varying lighting environment. Therefore, this paper proposes a novel framework for optimal spectrum design and control in multichannel LED lighting systems. It surpasses traditional methods by employing reinforcement learning (RL) to generate an entire optimal spectral power distribution (SPD) model, comprehensively considering CCT, CRI, CAF, and other relevant parameters. This framework integrates the RL-based optimal SPD generator with a corresponding real-time controller, bridging the gap between simulations and real-world applications. The effectiveness of this new framework has been verified by an experimental prototype, showcasing both the optimal SPD and the achieved control performance. This work paves the way for high-quality, user-centric lighting with dynamic CCT and spectral control, and enables the creation of lighting environments that optimize human well-being, productivity, and overall experience.